Lineamiento para evaluar la propuesta

With the rapid growth of advanced driver assistant and automated systems, the simulation tools have become an integral part of the development process of these systems. Not only the simulators can boost the development efficiency, but they also offer opportunities in analyzing scenarios that otherwise would have been extremely costly, time-consuming, and on some occasions dangerous to the human test subjects. In this work, we extended the co-simulation framework proposed in [14] and deployed it on the state-of-the-art game engine technology Unreal Engine 4 (UE4). With the help of NVIDIA PhysX and the UE4 physics engine, we were able to represent a large-scale traffic scenario with detailed 3D physics and visualization. We described the individual components of our platform in detail and proposed a simple human interpretable extension to the traditional car-following models. The extended model related a human driver to a feedback controller and employed the Intelligent Driver Model to supply a reference acceleration while incorporating a Fuzzy-PD system to compensate for the error. In this extended approach, we integrate the perception-reaction delay time of the driver as an individual component, along with descriptive driving characteristics through the parameters of the Intelligent Driver Model and fuzzy membership functions. Furthermore, we surveyed different driving characteristic and looked into a traffic dataset of a congested scenario to extract these various driving behaviors. We used the time headway and acceleration of the drivers as descriptive features of their driving characteristic. Using this information, we classified the drivers into three main classes, i.e., aggressive, normal, and conservative. We found that a gamma-distribution function can best describe the dispersal of the drivers within the environment. Finally, we incorporated all the extracted parameters into our

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simulator and equipped the vehicles with forward collision warning systems. We then analyzed the performance of these systems for each class of driver with a hard-braking response as driver reaction to a collision warning. Of the tested safety algorithms, driver-tuned NHTSA with Early warning configuration performed remarkably good for conservative drivers with 87% positive warnings generation and prevented all rear-end collisions. Conversely, none of the four configurations of forward collision warning systems showed adequate performance for the aggressive driving characteristics.

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APPENDIX A: A SCENARIO OF TWO VEHICLES ON A STRAIGHT

ROADWAY

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In this first appendix, we would like to briefly explain a typical driving situation that we frequently use throughout our evaluations and explanations of different topics. This scenario consists of two vehicles on a one-lane straight roadway with one vehicle following the other. We refer to the vehicle in back as the following (host) vehicle and the vehicle in front as the leader or leading vehicle. Many equations that appear in this work make use of information from the following and leading vehicle. In these equations, the following vehicle is index by 𝑖 and the leader is identified by 𝑖 − 1. Throughout our work, we use many variations of this specific two vehicle situation that best help us convey our message and understanding of the topic. Using our simulation platform that is discussed in chapter 2, we created different scenarios such as slow moving leader, decelerating leader, stopped leader, fast approaching follower, and distracted follower. The simplified illustration of the two vehicle scenario alongside variables of interest is shown in the Figure 35.

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In this appendix, we would like to provide the dynamical parameter of the considered vehicle. Throughout our simulation, we dealt with only one type of vehicle for which we had sufficient information. The vehicle we used throughout our simulations was a 2013-2014 Hyundai Santa Fe Sport. The 3D model of this vehicle was manually edited and rigged in Autodesk 3ds Max which was then imported into Unreal Engine 4 and was set up to be used with UE4 vehicle wrapper and NVIDIA PhysX Vehicle. A similar model of this vehicle can be downloaded for free at [51].

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The characteristic of the torque curve as well as some of the important mechanical and dynamical information of this vehicle that are used within our simulations are provided below.

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Table 9: General mechanical parameters of Hyundai Santa Fe

Mass, 𝑘𝑔 2510

Chassis width, 𝑐𝑚 188 Chassis height, 𝑐𝑚 155 Max engine 𝑅𝑃𝑀 6800 Moment of inertia of the

engine, 𝑘𝑔𝑚2 0.2 Differential type Open Front

Drive

Table 10: Transmission setup data Gear Switch Time 0.2 Gear auto box latency 3.0 Final ratio 3.51

Table 11: Gears setup data

Gear Setup Gear Ratio

Gear 1 4.651 Gear 2 2.831 Gear 3 1.842 Gear 4 1.386 Gear 5 1.0 Gear 6 0.772

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